<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:wfw="http://wellformedweb.org/CommentAPI/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	xmlns:atom="http://www.w3.org/2005/Atom"
	xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"
	xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
	>

<channel>
	<title>Data Science London</title>
	<atom:link href="http://datasciencelondon.org/feed/" rel="self" type="application/rss+xml" />
	<link>http://datasciencelondon.org</link>
	<description>Free, open dissemination of data science. The data science community</description>
	<lastBuildDate>Thu, 02 May 2013 09:29:55 +0000</lastBuildDate>
	<language>en-US</language>
	<sy:updatePeriod>hourly</sy:updatePeriod>
	<sy:updateFrequency>1</sy:updateFrequency>
	<generator>http://wordpress.org/?v=3.5.1</generator>
		<item>
		<title>Approximate Methods for Scalable Data Mining</title>
		<link>http://datasciencelondon.org/approximate-methods-for-scalable-data-mining/</link>
		<comments>http://datasciencelondon.org/approximate-methods-for-scalable-data-mining/#comments</comments>
		<pubDate>Thu, 02 May 2013 09:24:48 +0000</pubDate>
		<dc:creator></dc:creator>
				<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Data Scientist]]></category>
		<category><![CDATA[cardinality]]></category>
		<category><![CDATA[counting]]></category>
		<category><![CDATA[data mining]]></category>
		<category><![CDATA[probabilistic programming]]></category>

		<guid isPermaLink="false">http://datasciencelondon.org/?p=745</guid>
		<description><![CDATA[Andrew Clegg, Data Analytics &#038; Visualization Team Pearson Technology. Talk at Data Science London 24/04/13]]></description>
		<wfw:commentRss>http://datasciencelondon.org/approximate-methods-for-scalable-data-mining/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>PredictionIO –An Open Source Scalable Machine Learning Architecture</title>
		<link>http://datasciencelondon.org/predictionio-an-open-source-scalable-machine-learning-architecture/</link>
		<comments>http://datasciencelondon.org/predictionio-an-open-source-scalable-machine-learning-architecture/#comments</comments>
		<pubDate>Thu, 02 May 2013 09:20:45 +0000</pubDate>
		<dc:creator></dc:creator>
				<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Data Scientist]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[machine learning as a service]]></category>
		<category><![CDATA[mahout]]></category>
		<category><![CDATA[MLaaS]]></category>
		<category><![CDATA[prediction]]></category>
		<category><![CDATA[scalability]]></category>

		<guid isPermaLink="false">http://datasciencelondon.org/?p=748</guid>
		<description><![CDATA[Simon Chan @simonchannet Product Lead @PrdictionIO, talk at Data Science London @ds_ldn meetup 24/04/2013]]></description>
		<wfw:commentRss>http://datasciencelondon.org/predictionio-an-open-source-scalable-machine-learning-architecture/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Storm + Trident and The Holistic Architecture: Using Hadoop for batch and Storm for real time</title>
		<link>http://datasciencelondon.org/storm-trident-and-the-holistic-architecture-using-hadoop-for-batch-and-storm-for-real-time/</link>
		<comments>http://datasciencelondon.org/storm-trident-and-the-holistic-architecture-using-hadoop-for-batch-and-storm-for-real-time/#comments</comments>
		<pubDate>Thu, 02 May 2013 09:18:05 +0000</pubDate>
		<dc:creator></dc:creator>
				<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Data Scientist]]></category>
		<category><![CDATA[architecture]]></category>
		<category><![CDATA[hadoop]]></category>
		<category><![CDATA[real-time]]></category>
		<category><![CDATA[real-time analytics]]></category>
		<category><![CDATA[realtime]]></category>
		<category><![CDATA[storm]]></category>
		<category><![CDATA[trident]]></category>

		<guid isPermaLink="false">http://datasciencelondon.org/?p=743</guid>
		<description><![CDATA[Yodit Stanton, Freelance Data Scientist, Developer &#038; Systems Architect. Talk at Data Science London @ds-ldn, 24/0413]]></description>
		<wfw:commentRss>http://datasciencelondon.org/storm-trident-and-the-holistic-architecture-using-hadoop-for-batch-and-storm-for-real-time/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Reservoir computing: Adaptive online machine learning and neural networks</title>
		<link>http://datasciencelondon.org/reservoir-computing-adaptive-online-machine-learning-and-neural-networks/</link>
		<comments>http://datasciencelondon.org/reservoir-computing-adaptive-online-machine-learning-and-neural-networks/#comments</comments>
		<pubDate>Thu, 02 May 2013 09:15:05 +0000</pubDate>
		<dc:creator></dc:creator>
				<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Data Scientist]]></category>
		<category><![CDATA[ANN]]></category>
		<category><![CDATA[artificial neural networks]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[Neural Networks]]></category>
		<category><![CDATA[reservoir computing]]></category>

		<guid isPermaLink="false">http://datasciencelondon.org/?p=741</guid>
		<description><![CDATA[Neri van Otten, Data Scientist at Conversocial. Talk at Data Science London @ds_ldn meetup March 28th, 20123]]></description>
		<wfw:commentRss>http://datasciencelondon.org/reservoir-computing-adaptive-online-machine-learning-and-neural-networks/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Bayesian Neural Networks in Business Applications</title>
		<link>http://datasciencelondon.org/bayesian-neural-networks-in-business-applications/</link>
		<comments>http://datasciencelondon.org/bayesian-neural-networks-in-business-applications/#comments</comments>
		<pubDate>Thu, 02 May 2013 09:12:15 +0000</pubDate>
		<dc:creator></dc:creator>
				<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Data Scientist]]></category>
		<category><![CDATA[ANN]]></category>
		<category><![CDATA[Bayesian Networks]]></category>
		<category><![CDATA[Neural Networks]]></category>
		<category><![CDATA[NeuroBayes]]></category>

		<guid isPermaLink="false">http://datasciencelondon.org/?p=739</guid>
		<description><![CDATA[Johanna Fleckner, Project Manager &#038; Data Scientist at Blue-Yonder. Talk at Data Science London @ds_ldn meetup March 28th, 20123]]></description>
		<wfw:commentRss>http://datasciencelondon.org/bayesian-neural-networks-in-business-applications/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Area Under the Curve: Never too experienced to make a basic mistake</title>
		<link>http://datasciencelondon.org/area-under-the-curve-never-too-experienced-to-make-a-basic-mistake/</link>
		<comments>http://datasciencelondon.org/area-under-the-curve-never-too-experienced-to-make-a-basic-mistake/#comments</comments>
		<pubDate>Tue, 16 Apr 2013 16:27:51 +0000</pubDate>
		<dc:creator></dc:creator>
				<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Data Scientist]]></category>
		<category><![CDATA[#bdhldn]]></category>
		<category><![CDATA[data challenge]]></category>
		<category><![CDATA[data competition]]></category>
		<category><![CDATA[data science]]></category>
		<category><![CDATA[data scientist]]></category>
		<category><![CDATA[hackackathon]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[python]]></category>
		<category><![CDATA[R]]></category>

		<guid isPermaLink="false">http://datasciencelondon.org/?p=730</guid>
		<description><![CDATA[I was one of the 170 or so people [220 actual count] at the Data Science London Hackathon over the weekend. As always this was well run by Carlos and his team who kept us fed, watered and connected to the Internet. One of the three challenges involved a dataset containing pairs of Twitter users, [...]]]></description>
		<wfw:commentRss>http://datasciencelondon.org/area-under-the-curve-never-too-experienced-to-make-a-basic-mistake/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Machine Learning in Python with scikit-learn @ds_ldn meetup</title>
		<link>http://datasciencelondon.org/machine-learning-python-scikit-learn-ipython-dsldn-data-science-london-kaggle/</link>
		<comments>http://datasciencelondon.org/machine-learning-python-scikit-learn-ipython-dsldn-data-science-london-kaggle/#comments</comments>
		<pubDate>Sat, 09 Mar 2013 12:19:05 +0000</pubDate>
		<dc:creator></dc:creator>
				<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Data Scientist]]></category>
		<category><![CDATA[ipython]]></category>
		<category><![CDATA[kaggle]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[parallel Python]]></category>
		<category><![CDATA[python]]></category>
		<category><![CDATA[scikit-learn]]></category>

		<guid isPermaLink="false">http://datasciencelondon.org/?p=706</guid>
		<description><![CDATA[&#160; Machine Learning with scikit-learn Talk by Andreas Mueller @t3kcit at Data Science London @ds_ldn 07/03/13 &#160; &#160; iPython Notebook click here &#160; Parallel and Large Scale Machine Learning with scikit-learn Talk by Olivier Grisel @ogrisel at Data Science London @ds_ldn 07/03/13 &#160; &#160; iPython Notebook click here &#160; Getting started with scikit-learn, tutorial and [...]]]></description>
		<wfw:commentRss>http://datasciencelondon.org/machine-learning-python-scikit-learn-ipython-dsldn-data-science-london-kaggle/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>&#8220;Social Media Intelligence Text, Network Mining and Predictive Analytics Combined&#8221; by Phil Winters @Knime</title>
		<link>http://datasciencelondon.org/social-media-intelligence-text-network-mining-and-predictive-analytics-combined-by-phil-winters-knime/</link>
		<comments>http://datasciencelondon.org/social-media-intelligence-text-network-mining-and-predictive-analytics-combined-by-phil-winters-knime/#comments</comments>
		<pubDate>Mon, 18 Feb 2013 11:55:17 +0000</pubDate>
		<dc:creator></dc:creator>
				<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Data Scientist]]></category>
		<category><![CDATA[clustering]]></category>
		<category><![CDATA[Knime]]></category>
		<category><![CDATA[network mining]]></category>
		<category><![CDATA[predictive analytics]]></category>
		<category><![CDATA[R]]></category>
		<category><![CDATA[sentiment analysis]]></category>
		<category><![CDATA[social media]]></category>

		<guid isPermaLink="false">http://datasciencelondon.org/?p=697</guid>
		<description><![CDATA[Talk by Phil Winters, Data Whisperer @Knime Data Science London @ds_ldn meetup on 12/02/2013]]></description>
		<wfw:commentRss>http://datasciencelondon.org/social-media-intelligence-text-network-mining-and-predictive-analytics-combined-by-phil-winters-knime/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>&#8220;Recommender Systems in R&#8221; by Tamas Jambor @Sky</title>
		<link>http://datasciencelondon.org/recommender-systems-in-r-by-tamas-jambor-sky/</link>
		<comments>http://datasciencelondon.org/recommender-systems-in-r-by-tamas-jambor-sky/#comments</comments>
		<pubDate>Mon, 18 Feb 2013 11:51:48 +0000</pubDate>
		<dc:creator></dc:creator>
				<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Data Scientist]]></category>
		<category><![CDATA[media]]></category>
		<category><![CDATA[R]]></category>
		<category><![CDATA[recommendations]]></category>
		<category><![CDATA[recommender systems]]></category>
		<category><![CDATA[recsys]]></category>

		<guid isPermaLink="false">http://datasciencelondon.org/?p=694</guid>
		<description><![CDATA[Talk by Tamas Jambor, Data Scientist @Sky Data Science London @ds_ldn meetup on 12/02/2013]]></description>
		<wfw:commentRss>http://datasciencelondon.org/recommender-systems-in-r-by-tamas-jambor-sky/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>&#8220;Big Data Science Challenges in Media&#8221; by by Chandan Rajah @Sky</title>
		<link>http://datasciencelondon.org/big-data-science-challenges-in-media/</link>
		<comments>http://datasciencelondon.org/big-data-science-challenges-in-media/#comments</comments>
		<pubDate>Mon, 18 Feb 2013 11:49:29 +0000</pubDate>
		<dc:creator></dc:creator>
				<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Data Scientist]]></category>
		<category><![CDATA[big data]]></category>
		<category><![CDATA[data quality]]></category>
		<category><![CDATA[data science]]></category>
		<category><![CDATA[ETL]]></category>
		<category><![CDATA[feature extraction]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[media]]></category>
		<category><![CDATA[Scala]]></category>
		<category><![CDATA[Scalding]]></category>

		<guid isPermaLink="false">http://datasciencelondon.org/?p=692</guid>
		<description><![CDATA[Talk by Chandan Rajah, Chief Architect Big Data @Sky Data Science London @ds_ldn meetup on 12/02/2013]]></description>
		<wfw:commentRss>http://datasciencelondon.org/big-data-science-challenges-in-media/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
	</channel>
</rss>
